Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.

PLoS computational biology - 1 May 2022

Lai Boqiao, Qian Sheng, Zhang Hanwei, Zhang Siwei, Kozlova Alena, Duan Jubao, Xu Jinbo, He Xin

Abstract excerpt

Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Epigenomics
Share to Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Neural Networks, Computer

Share to Polymorphism, Single Nucleotide

Source

Open in PubMed Open full text

Identifiers

PMID: 35576194
DOI: 10.1371/journal.pcbi.1010011
PMCID: PMC9135341

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.PMID 35576194Read ↗

Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer LearningSemantic proximity Read ↗

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Semantic proximity Read ↗

Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome.Semantic proximity Read ↗

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomicsSemantic proximity Read ↗

DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.Semantic proximity Read ↗

Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsSemantic proximity Read ↗

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networksSemantic proximity Read ↗

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.Semantic proximity Read ↗

Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer.Semantic proximity Read ↗

Decoding common and rare noncoding variant effects across cellular and developmental contexts.Semantic proximity Read ↗

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Semantic proximity Read ↗

BRAIN-MAGNET: A novel functional genomics atlas coupled with convolutional neural networks facilitates clinical interpretation of disease relevant variants in non-coding regulatory elementsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.